CIP Genetics and Microbiome Core

CIP 遗传学和微生物组核心

• 批准号: 9556835
• 负责人: Colm Ohuigin
• 金额: $ 200.13万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9556835
• 关键词: APOL1 gene; Affect; Area; Autoimmune Diseases; Autoimmune Process; Base Pairing; Base Sequence; Behavior; Bioinformatics; Biological Assay; Biomass; Characteristics; Clinical; Cohort Analysis; Colon Carcinoma; Communicable Diseases; Computer Analysis; Computer software; Copy Number Polymorphism; Crohn' s disease; DNA; Data; Databases; Disease; Epidemiology; Feces; Gene Expression; Genes; Genetic; Genome; Genomic approach; Genotype; Graft Rejection; HIV; HIV Infections; HIV/HCV; HLA Antigens; HLA-B Antigens; HLA-C Antigens; HLA-DP Antigens; Health; Human; Human Papillomavirus; Immune; Immunologics; Infection; Inflammation; Inflammatory; Institutes; Kaposi Sarcoma; Kidney Diseases; Libraries; Malignant Neoplasms; Metabolic; Metabolic Pathway; Metagenomics; Methods; Microbe; Microbial Genome Sequencing; Mission; Mutation; Noise; Nucleic Acids; Outcome; Paper; Plasmids; Preparation; Process; Production; Promoter Regions; RNA; Reproducibility; Research; Research Personnel; Ribosomal RNA; Risk; Robotics; Role; Running; Sampling; Scientist; Sequence Analysis; Shotguns; Signal Transduction; Somatic Mutation; Source; Spectrum Analysis; Statistical Data Interpretation; Support Groups; System; Testing; United States National Institutes of Health; Variant; Virus; Work; cloud platform; cohort; comparative; computer center; epigenetic regulation; experimental study; genetic analysis; genetic element; genome sequencing; genomic data; graft vs host disease; high throughput analysis; insight; interest; killer immunoglobulin-like receptor; metagenome; microbial; microbial genome; microbiome; microbiota; mouse model; nutrition; programs; rRNA Genes; transcriptome sequencing; whole genome; working group

The Microbiome and Genetics core (MGC) of the Cancer and Inflammation Program (CIP) runs its microbiome facility in Building 37 of Bethesda with a small team consisting of a research technician, three bioinformaticians and one scientist. The primary function is to meet the growing interest and challenges of characterizing the role of the microbiota in cancer and inflammatory processes. Having established reliable and reproducible methods to isolate and characterize nucleic acids of microbiota isolated from feces and other sources, the core has worked with a range of source materials and PIs to help effectively determine changes in microbial representation between experimental samples. Over the past year the core has expanded its process repertoire, adding whole genome sequencing of microbial isolates as well as shotgun metagenomics to its repertoire of 16S amplicon metagenomics. We have isolated DNA from a variety of mammalian sources. These additions enable the core to look at potential metabolic pathway changes induced by changes in gene content and composition of the microbiota. Robotic sample preparation platforms (Eppendorf 5073 and 5075) are used to maximize throughput and reproducibility, both for nucleic acid isolation and for barcoded library preparation. Quantification is accomplished using qPCR or spectroscopy. Following purification, barcoding and quantification, an Illumina MiSeq is used to sequence amplicons of 16S rRNA genes. For genomic approaches, the same DNA isolation process is used and as little as 1ng of DNA is subjected to breakage and library preparation by transposon driven 'tagmentation'. Whole genome sequencing from isolates is done on the Miseq platform and shotgun metagenomes of the microbiota are run on the higher capacity Nexseq. In the past year samples from more than 40 projects have been processed from inside CIP and NCI as well as for collaborators from other NIH institutes and almost 1Tb of sequenced base pairs of data generated and analyzed from these platforms. Across the projects, different challenges ranging from how to isolate DNA from high or from lower bacterial biomass sources, how to partition analyses from different sources and which treatments maximize the signal to noise ratio of experiments have been met successfully. We are handling samples associated with both clinical and with basic scientific research. The bioinformatic challenges began with storage, delivery and backup of large amounts of information. This was achieved using both Illumina's cloud server as well as a backup system at the computer center of FNLCR. We continue to make available two analytical approaches to determining microbial abundances, the Qiime and mothur platforms and have tested them extensively. Our favored pipeline to take advantage of components of each. The analyses are also limited by the quality of databases of ribomsomal RNA. We continue to develop a database of fully vetted, high quality rRNA sequences for use in identifying components of the microbiome in samples. For shotgun metagenomics, we are using two exploratory pipelines, the publically available HUMAnN2 and one developed in-house to explore the bioinformatics challenges in going from defined amplicon targets such as rRNA to whole genome or transcriptome sequencing. We are using software (Picrust, Pathoscope) that offers insights into the genomic data generated and also have explored metabolic pathways using MetaPhlAn. We have sequenced and fully assembled genomes of Corynebacterial isolates and fully annotated the genes and plasmids of the isolates using the prokka software. Our work has been recognized by coauthorships with collaborators or acknowledgements elsewhere. We continue to support analysis in genetics of HLA expression. We have been involved in the production of papers determining the characteristics of promoter regions of HLA-A, -B and -C in relation to expression of these genes; in epigenetic regulation of HLA-A expression; in showing a role for HLA-DP expression in graft-versus-host disease; in determining risk of Kaposi Sarcoma for particular HLA and KIR combinations and also in characterizing the role of APOL1 variants in kidney disease. These studies build upon our work in helping to show that expression affects outcomes infectious and autoimmune disease such as HIV and infection and well as autoimmune related conditions such as Crohn disease or even transplant rejection. The genetic elements that control immune gene expression are of considerable interest and we continue to support groups working on their characterization.

癌症和炎症计划（CIP）的微生物组和遗传学核心（MGC）在贝塞斯达（Bethesda）的37号建筑中运营了其微生物组设施，其中包括一个由一名研究技术员，三位生物信息学家和一名科学家组成的小团队。主要功能是应对表征菌群在癌症和炎症过程中的作用的日益增长的兴趣和挑战。在建立了可靠且可重复的方法以分离并表征从粪便和其他来源分离的微生物核酸的核酸，该核心已与一系列源材料和PIS合作，以有效地确定实验样品之间微生物表示的变化。在过去的一年中，核心扩大了其过程库，增加了微生物分离株的整个基因组测序以及shot弹枪宏基因组学的整个基因组测序，并将其曲目添加到16S Amplicon元基因组学的曲目中。我们从各种哺乳动物来源中有分离的DNA。这些添加使核心能够查看基因含量变化和菌群组成引起的潜在代谢途径变化。机器人样品制备平台（Eppendorf 5073和5075）用于最大化吞吐量和可重复性，包括核酸分离和条形码的文库制备。使用QPCR或光谱法完成定量。纯化，条形码和定量后，使用Illumina Miseq来对16S rRNA基因的扩增子进行测序。对于基因组方法，使用相同的DNA隔离过程，仅1NG DNA就可以通过转座子驱动的“标记”来进行断裂和文库制备。来自分离株的整个基因组测序是在Miseq平台上完成的，而微生物群的shot弹枪元基因组在更高容量的NexSeq上进行。在过去的一年中，来自内部CIP和NCI的40多个项目的样本以及其他NIH研究所的合作者以及从这些平台生成和分析的几乎1TB的测序基本对。在整个项目中，从如何将DNA与高细菌生物量来源分离的不同挑战，如何从不同来源分析分析以及哪些处理能够最大程度地提高实验的信号与噪声比的最大程度。我们正在处理与临床和基础科学研究相关的样本。生物信息学挑战始于大量信息的存储，交付和备份。这是使用Illumina的云服务器以及FNLCR计算机中心的备份系统实现的。我们继续提供两种分析方法来确定微生物丰度，Qiime和Mothur平台，并对其进行了广泛的测试。我们喜欢的管道利用了每个组件。分析也受到核腺体RNA数据库质量的限制。我们继续开发一个完全审查的高质量rRNA序列的数据库，用于识别样品中微生物组的组件。对于shot弹枪宏基因组学，我们使用了两个探索性管道，即公开可用的Humann2和一个在内部开发的，以探索从定义的扩增子目标（例如RRNA）到整个基因组或转录组测序的生物信息学挑战。我们正在使用软件（PICRUST，病原体），该软件可对生成的基因组数据进行见解，并使用mentaphlan探索了代谢途径。我们已经使用prokka软件测序并完全组装了Corynebacterial分离株的基因组，并完全注释了分离株的基因和质粒。我们的工作得到了与其他地方合作者或致谢的共同作品的认可。我们继续支持HLA表达遗传学的分析。我们参与了与这些基因表达有关的HLA -A，-b和-c启动子区域的特征的论文生产。在HLA-A表达的表观遗传调节中；在展示HLA-DP表达在移植物抗宿主疾病中的作用；在确定特定HLA和KIR组合的Kaposi肉瘤的风险时，以及在肾脏疾病中的APOL1变体的作用方面。这些研究以我们的工作为基础，帮助表明表达影响了感染和自身免疫性疾病，例如艾滋病毒和感染以及与自身免疫性疾病，例如克罗恩病甚至移植抑制。控制免疫基因表达的遗传元素具有很大的兴趣，我们继续支持从事其表征的群体。